POS1051 LUNG CLUSTERING ANALYSIS-BASED PHENOTYPES OF RHEUMATOID ARTHRITIS USING ARTIFICIAL INTELLIGENCE-BASED TECHNOLOGY FOR CHEST COMPUTED TOMOGRAPHY
Authors: Y. Nakayama, R. Nakashima, T. Handa, K. Tanizawa, H. Onizawa, T. Fujii, K. Murata, K. Murakami, A. Onishi, M. Tanaka, M. Shirakashi, R. Hiwa, H. Tsuji, K. Kitagori, S. Akizuki, H. Yoshifuji, A. Morinobu
Journal: Annals of the Rheumatic Diseases (2023)

<h3>Background</h3> Lung involvement is a prevalent extraarticular manifestation of rheumatoid arthritis (RA) that remains a significant clinical challenge. Few studies have comprehensively quantified lung abnormalities of RA patients using artificial intelligence-based (AI) technology. <h3>Objectives</h3> The aim of this study was to quantify lung lesions in RA patients and classify them based on their lung parameters. <h3>Methods</h3> An AI-based quantitative computed tomography (CT) image analysis software (AIQCT) was applied to high-resolution CT scans of RA patients in a cross-sectional manner. AIQCT automatically classified and quantified 10 types of parenchymal image patterns, expressing the volumes as percentages of total lung volume [1]. Hierarchical Ward's linkage clustering based on these patterns identified five clusters. Visual assessments (ILD, and airway lesions) of HRCT and clinical phenotypes were assessed. <h3>Results</h3> A total of 408 RA patients were included in the study. The lung profiles of the five clusters were as follows: Cluster I (68.6%), characterized by nearly normal lungs; Cluster II (23.5%), characterized by slight lung lesions with honeycombs or ground-glass opacities (GGOs); Cluster III (5.6%), characterized by the predominance of GGOs; Cluster IV (1.0%), characterized by a predominant hyperlucent area; and Cluster V (1.2%), characterized by extensive lung abnormalities (Figure 1, Table 1). The number of patients in each cluster with ILD and airway lesions, based on visual assessments, were as follows: [ILD] Cluster I, 11/280 (3.9%), Cluster II, 19/96 (19.8%), Cluster III, 5/23 (21.7%), Cluster IV, 0/4 (0%), and Cluster V, 5/5 (100%) (p &lt; 0.001): [airway lesions] Cluster I, 31/280 (11.1%), Cluster II, 29/96 (30.2%), Cluster III, 6/23 (26.1%), Cluster IV, 0/4 (0%), and Cluster V, 1/5 (20%) (p &lt; 0.001). Clinical characteristics of each cluster were described in Table 1. The disease activity of RA in each cluster were as follows: [DAS28ESR, mean (SD)] Cluster I, 2.4 (1.0), Cluster II, 3.0 (1.2), Cluster III, 2.8 (1.0), Cluster IV, 2.2 (0.7), and Cluster V, 3.0 (0.8) (p &lt; 0.001); [HAQ, mean (SD)] Cluster I, 0.4 (0.6), Cluster II, 0.9 (0.9), Cluster III, 1.1 (0.9), Cluster IV, 0.3 (0.3), and Cluster V, 0.8 (0.9) (p &lt; 0.001). <h3>Conclusion</h3> This study is the first to classify lung lesions in comprehensive RA patients using quantitative data derived from novel AI technology. The AIQCT-derived clustering of RA patients appears to be associated with their clinical backgrounds and characteristics. <h3>Reference</h3> [1] Ann Am Thorac Soc 2022;19:399-406 <h3>Acknowledgements:</h3> NIL. <h3>Disclosure of Interests</h3> Yoichi Nakayama: None declared, Ran Nakashima: None declared, Tomohiro Handa: None declared, Kiminobu Tanizawa: None declared, Hideo Onizawa: None declared, Takayuki Fujii: None declared, Koichi Murata Speakers bureau: AbbVie GK, Eisai Co., Ltd., Pfizer Inc., Chugai Pharmaceutical Co., Ltd., Mitsubishi Tanabe Pharma Corporation, Pfizer Inc., Bristol-Myers Squibb, Daiichi Sankyo Co. Ltd., and Asahi Kasei Pharma Corp., Grant/research support from: Daiichi Sankyo Co. Ltd, Kosaku Murakami: None declared, Akira Onishi: None declared, Masao Tanaka: None declared, Mirei Shirakashi: None declared, Ryosuke Hiwa: None declared, Hideaki Tsuji: None declared, Koji Kitagori: None declared, Syuji Akizuki: None declared, Hajime Yoshifuji: None declared, Akio Morinobu: None declared.